Proceedings of the 3rd European Conference on Intellectual Capital

Sorin Anagnoste and Gabriela Dumitru
transformational leadership, which we will go into more details later in <strong>the</strong> paper. The top and middle
management started to be younger and younger, and this research can be <strong>the</strong> start <strong>of</strong> a second part
in <strong>the</strong> near future due to <strong>the</strong> rapid transformation <strong>of</strong> <strong>the</strong> markets, especially when important events
occur more <strong>of</strong>ten than ever. The research can be an answer to <strong>the</strong> suggestion <strong>of</strong> Kabac<strong>of</strong>f (2002)
regarding <strong>the</strong> personal characteristics, such age, education, etc that have a significant impact on
leadership. For example, <strong>the</strong> youngest manager within Heineken Romania is under 30 years old and
this can be an answer to a lot <strong>of</strong> questions, especially when you want to improve <strong>the</strong> management <strong>of</strong>
<strong>the</strong> human resources.
Therefore, <strong>the</strong> present work attempts a comprehensive research <strong>of</strong> transformational leadership <strong>of</strong>
managers from two perspectives, personal and organizational dimensions, to describe new both <strong>the</strong>
individual and also <strong>the</strong> impact <strong>of</strong> <strong>the</strong>se variables on leadership practice.
Also, <strong>the</strong> respondents were also asked to indicate <strong>the</strong>ir last form <strong>of</strong> education completed: 1) Highschool,
2) University, 3) Master, 4) PhD, 5) MBA
The rating scale adopted was Likert-type scale from 1 (Strongly Disagree), 2 (Partially Disagree), 3
(Uncertain), 4 (Partially Agree), 5 (Strongly agree). Some authors, like Hogan and Hogan (2001) are
not in favor <strong>of</strong> self-report data used in leadership research, because <strong>the</strong>y consider that “leadership is
a social influence process and thus, should be determined by manager’ s staff/direct reports”. (Hogan,
2005,p.3). Moreover, while Gill (2003) accepts that self-reports are open to criticism, he argued that
<strong>the</strong>y could be valid and very important in certain circumstances. Bass (2002) has demonstrated how
“self-report personality scales show predictable, significant and substantial correlations with criteria <strong>of</strong>
management job success”.
6. SPSS interpretation
The first step <strong>of</strong> <strong>the</strong> analysis is to determine whe<strong>the</strong>r <strong>the</strong> twenty-eight statements represent
identifiable factors. The method used for this objective is factor analysis. The analysis was initiated
with <strong>the</strong> assumption that all variables are correlated to some extent. Therefore, those variables that
share similar underlying dimensions should be highly correlated, and those variables that measure
dissimilar dimensions should yield low correlations. The adequacy <strong>of</strong> <strong>the</strong> correlation matrix is
measured with <strong>the</strong> help <strong>of</strong> <strong>the</strong> KMO (0.76, which is over 0.6; KMO measures <strong>the</strong> sampling adequacy
which should be greater than 0.6 for a satisfactory factor analysis to proceed) and Bartlett test that
yield a valued <strong>of</strong> 913.522 and an associated level <strong>of</strong> significance smaller than 0.001. Thus, <strong>the</strong>
correlation matrix has significant correlations among at least some <strong>of</strong> <strong>the</strong> variables.
Once applied <strong>the</strong> factor analysis with main component extraction through varimax orthogonal rotation
were obtained 7 factors that represent 61.3 percent <strong>of</strong> <strong>the</strong> information contained in <strong>the</strong> original data.
Applying an oblimin rotation also shows <strong>the</strong> same results. Table 2 presents <strong>the</strong> percentage <strong>of</strong> each
factor accounted in total variance.
Component
Total
Initial Eigenvalues
% <strong>of</strong> Variance Cumulative %
1 7.444 26.584 26.584
2 2.062 7.366 33.950
3 1.797 6.417 40.367
4 1.705 6.091 46.458
5 1.511 5.397 51.855
6 1.343 4.797 56.652
7 1.302 4.649 61.301
Figure 6: Total variance explained
After a first analysis <strong>of</strong> <strong>the</strong> factors extracted in <strong>the</strong> varimax rotation we have decided to rerun <strong>the</strong>
analysis and impose <strong>the</strong> condition to have a fixed number <strong>of</strong> five factors extracted because <strong>the</strong>
difference between <strong>the</strong> fifth and sixth is considerable. The initial rotated factor structure showed a
number <strong>of</strong> cross-loaded items. Deletion <strong>of</strong> cross-loaded items serves to clarify <strong>the</strong> factors and makes
<strong>the</strong>ir interpretation easier.
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